Recovering of weather degraded images based on RGB response ratio constancy

Luzón-González, Raúl; Nieves, Juan Luis; Romero, Javier

doi:10.1364/ao.54.00b222

Cited by 26 publications

(21 citation statements)

References 34 publications

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“…The assortment of priors has induce to the performance of [8] that explore different chunk features in a research framework. Rather of small chunks, in [9] the image is irregular into regions with Indistinguishable distances, and the contrast is Lengthen within each Section This may create artifacts at the Boundaries between segments. In [25,17] , color lines are fitted in RGB space per patch , looking for small patches with constant transmission .…”

Section: Related Previus Workmentioning

confidence: 99%

Evolution of New Integrated Haze Removal Algorithm Based on Haze Line

Meenu

Kumar²,

Panchal³

et al. 2019

IJEAT

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Haze is a standout amongst the most essential factor that debases the outside pictures. Picture corruption relies upon the separation of the Object from the camera as the separation increment from the camera of the scene point haze additionally increases. This paper presents a haze removal technique which is relies on the Haze-line prior that is recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the airlight proposed approach is known as haze line. In this paper we proposed Post processing CBMG (Contrast, Brightness, Midtone and Gama adjustment) followed by four stages: clustering the pixel into haze line, Estimation of Transmission map, Regularization, Dehazing, This method is implement by observation of two basic things first pixels in a given group are regularly non local . Second pictures with improved perceivability (or sunny morning picture) have more differentiation than the pictures stopped by terrible climate. Experimental result demonstrate that our method remove haze layer and provide qualitative result.

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Section: Related Previus Workmentioning

confidence: 99%

Evolution of New Integrated Haze Removal Algorithm Based on Haze Line

Meenu

Kumar²,

Panchal³

et al. 2019

IJEAT

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show abstract

“…A(1 − t(x)) is the interference term caused by the scattering of ambient light, and increases with the increasing of depth. In order to solve the ill posed problem that the number of unknown variables is more than the number of equations in (1), a lot of defogging algorithms by physical models are based on a prior or assumption to estimate t(x) and A [12]- [16].…”

Section: Atmosphere Scattering Modelmentioning

confidence: 99%

“…Thus, combining (16) and 17yields the energy model for the transferring coefficient K* as shown in the following equation:…”

Section: Random Walk Modelmentioning

confidence: 99%

Novel Defogging Algorithm Based on the Joint Use of Saturation and Color Attenuation Prior

Du-yan

2018

IEICE Trans. Inf. & Syst.

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Focusing on the defects of famous defogging algorithms for fog images based on the atmosphere scattering model, we find that it is necessary to obtain accurate transmission map that can reflect the real depths both in large depth and close range. And it is hard to tackle this with just one prior because of the differences between the large depth and close range in foggy images. Hence, we propose a novel prior that simplifies the solution of transmission map by transferring coefficient, called saturation prior. Then, under the Random Walk model, we constrain the transferring coefficient with the color attenuation prior that can obtain good transmission map in large depth regions. More importantly, we design a regularization weight to balance the influences of saturation prior and color attenuation prior to the transferring coefficient. Experimental results demonstrate that the proposed defogging method outperforms the state-of-art image defogging methods based on single prior in terms of details restoring and color preserving.

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“…The focus is to perform dehazing on images captured using a conventional camera with no special hardware setup. Luzón González et al [11] recovered weather degraded image using Red-Green-Blue (RGB) response ratio constancy by segmenting depth regions based on clustering. In general, such clustering may lead to erroneous results and is also time-consuming.…”

Section: Introductionmentioning

confidence: 99%